Zhang, L, Li, K orcid.org/0000-0001-6657-0522, Du, D et al. (2 more authors) (2017) State-of-Charge Estimation of Lithium Batteries Using Compact RBF Networks and AUKF. In: Li, K, Xue, Y, Cui, S, Niu, Q, Yang, Z and Luk, P, (eds.) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. LSMS 2017: International Conference on Life System Modeling and Simulation and ICSEE 2017: International Conference on Intelligent Computing for Sustainable Energy and Environment, 22-24 Sep 2017, Nanjing, China. Springer , pp. 396-405. ISBN 978-981-10-6363-3
Abstract
A novel framework for the state-of-charge (SOC) estimation of lithium batteries is proposed in this paper based on an adaptive unscented Kalman filters (AUKF) and radial basis function (RBF) neural networks. Firstly, a compact off-line RBF network model is built using a two-stage input selection strategy and the differential evolution optimization (TSS_DE_RBF) to represent the dynamic characteristics of batteries. Here, in the modeling process, the redundant hidden neurons are removed using a fast two-stage selection algorithm to further reduce the model complexity, leading a more compact model in line with the principle of parsimony. Meanwhile, the nonlinear parameters in the radial basis function are optimized through the differential evolution (DE) method simultaneously. The method is implemented on a lithium battery to capture the nonlinear behaviours through the readily measurable input signals. Furthermore, the SOC is estimated online using the AUKF along with an adaptable process noise covariance matrix based the developed RBF neural model. Experimental results manifest the accurate estimation abilities and confirm the effectiveness of the proposed approach.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Keywords: | State-of-charge (SOC); Two-stage selection (TSS); Radial basis function (RBF); Differential evolution (DE); Adaptive unscented Kalman filter (AUKF) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Nov 2018 11:30 |
Last Modified: | 06 Mar 2019 14:26 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-981-10-6364-0_40 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139086 |